Abstract

Predicting how transcription factors (TFs) interpret regulatory sequences to control gene expression remains a major challenge. Past studies have primarily focused on native or engineered sequences, and thus remained limited in scale. Here, we use random sequences as an alternative, measuring the expression output of nearly 100 million synthetic yeast promoters comprised of random DNA. Random sequences yield a broad range of reproducible expression levels, indicating that the fortuitous binding sites in random DNA are functional. From this data we learn 'billboard' models of transcriptional regulation that explain 93% of expression variation of test data, recapitulate the organization of native chromatin in yeast, and help refine cis-regulatory motifs. Analyzing the residual variation, we uncover more complex regulatory mechanisms, such as strand, position, and helical face preferences of TFs. Such high-throughput regulatory assays of random DNA provide the large-scale data necessary to learn complex models of cis-regulatory logic.